Cobalt-rich crust resource distribution circle classification method and system, electronic equipment and storage medium

Abstract

The application discloses a cobalt-rich crust resource distribution circle division method and system, an electronic device and a storage medium. The method comprises the following steps: obtaining preset geological sedimentary environment data; wherein the preset geological sedimentary environment data comprises oceanic crust age data, sedimentation rate data and preset near-bottom shallow stratigraphic profile data; determining first region data according to the oceanic crust age data and the sedimentation rate data through a preset discrimination model; wherein the preset discrimination model is constructed by the coupling effect of a sedimentation rate parameter and an oceanic crust age parameter; performing topographic constraint correction on the first region data according to a preset topographic constraint parameter to obtain second region data; and performing crust region analysis according to the second region data and the preset near-bottom shallow stratigraphic profile data to obtain a target cobalt-rich crust region. The embodiment of the application can effectively improve the efficiency and accuracy of cobalt-rich crust circle division. The application can be widely applied to the field of resource development.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of resource development, and in particular to a cobalt-rich crust resource distribution circle division method and system, an electronic device, and a storage medium. BACKGROUND

[0002] Cobalt-rich ferromanganese crusts (hereinafter referred to as crusts or cobalt-rich crusts) appear on the hard rock basement of the entire ocean basin. They are formed on the sea floor of the flanks of seamounts, ridges and plateaus, and the top of the peak, and are rich in Co, Ti, Mn, Ni, Pt, Zr, Nb, Te, Bi, Mo, W, Th and rare earth elements. The exploration of cobalt-rich crusts requires the use of various technical means such as geology, geophysics, and geochemistry, and various technical methods need to be closely combined. In the related art, the circle division of the cobalt-rich crust development area is mainly based on the identification of acoustic and optical data, but the corresponding circle division precision is low, it is difficult to accurately divide the cobalt-rich crust development area, and the work efficiency is not high.

[0003] To sum up, the technical problems existing in the related art need to be improved. SUMMARY

[0004] The main purpose of the embodiments of the present application is to provide a cobalt-rich crust resource distribution circle division method and system, an electronic device, and a storage medium, which can effectively improve the efficiency and accuracy of cobalt-rich crust circle division.

[0005] To achieve the above-mentioned purpose, one aspect of the embodiments of the present application provides a cobalt-rich crust resource distribution circle division method, which comprises the following steps:

[0006] obtaining preset geological sedimentary environment data; wherein the preset geological sedimentary environment data includes oceanic crust age data, sedimentation rate data, and preset near-bottom shallow stratigraphic section data;

[0007] determining first area data according to the oceanic crust age data and the sedimentation rate data through a preset discrimination model; wherein the preset discrimination model is constructed by the coupling effect of a sedimentation rate parameter and an oceanic crust age parameter;

[0008] performing topographic constraint correction on the first area data according to a preset topographic constraint parameter to obtain second area data;

[0009] performing crust area analysis according to the second area data and the preset near-bottom shallow stratigraphic section data to obtain a target cobalt-rich crust area.

[0010] In some embodiments, before performing the step of determining first area data according to the oceanic crust age data and the sedimentation rate data through a preset discrimination model, the method comprises:

[0011] combining the sedimentation rate parameter and the oceanic crust age parameter to determine the coupling effect;

[0012] determining a model discrimination condition through the coupling effect to construct the preset discrimination model.

[0013] In some embodiments, the determining the first region data through the preset discrimination model according to the oceanic crust age data and the sedimentation rate data comprises:

[0014] performing a first classification processing on the oceanic crust age data through a preset age threshold to obtain oceanic crust classification data;

[0015] performing a second classification processing on the sedimentation rate data through a preset sedimentation threshold to obtain sedimentation classification data;

[0016] performing region screening through the preset discrimination model according to the oceanic crust classification data and the sedimentation classification data to obtain the first region data.

[0017] In some embodiments, the performing terrain constraint correction on the first region data according to a preset terrain constraint parameter to obtain second region data comprises:

[0018] performing buffer zone analysis according to the first region data and region terrain data to obtain third region data;

[0019] performing region screening on the third region data through the preset terrain constraint parameter to obtain the second region data; wherein the preset terrain constraint parameter comprises a region slope threshold.

[0020] In some embodiments, the performing crust region analysis according to the second region data and the preset near-bottom shallow stratum profile data to obtain a target cobalt-rich crust region comprises:

[0021] determining target region longitude and latitude information according to the second region data;

[0022] performing region position constraint on the preset near-bottom shallow stratum profile data through the target region longitude and latitude information to obtain the target cobalt-rich crust region.

[0023] In some embodiments, after performing the region position constraint on the preset near-bottom shallow stratum profile data through the target region longitude and latitude information to obtain the target cobalt-rich crust region, the method further comprises:

[0024] extracting preset visualization data from the second region data according to the target cobalt-rich crust region; wherein the preset visualization data comprises longitude data, latitude data, depth data and amplitude intensity data;

[0025] According to the preset visualization data, three-dimensional ore body visualization is performed to obtain a three-dimensional image of the cobalt-rich crust.

[0026] In some embodiments, after performing the crust region analysis according to the second region data and the preset near-bottom shallow stratum profile data to obtain a target cobalt-rich crust region, the method further comprises:

[0027] Collecting preset ground live data corresponding to the target cobalt-rich crust region; wherein the preset ground live data includes drilling data and camera data;

[0028] According to the drilling data and the camera data, the target cobalt-rich crust region is verified to adjust the preset discrimination model through the corresponding verification result.

[0029] To achieve the above-mentioned purpose, another aspect of the embodiment of the present application proposes a cobalt-rich crust resource distribution circle drawing system, the system comprises:

[0030] The first module is configured to obtain preset geological sedimentary environment data; wherein the preset geological sedimentary environment data includes oceanic crust age data, sedimentation rate data and preset near-bottom shallow stratum profile data;

[0031] The second module is configured to determine first region data according to the oceanic crust age data and the sedimentation rate data through a preset discrimination model; wherein the preset discrimination model is constructed by the coupling effect of the sedimentation rate parameter and the oceanic crust age parameter;

[0032] The third module is configured to perform topographic constraint correction on the first region data according to preset topographic constraint parameters to obtain second region data;

[0033] The fourth module is configured to perform crust region analysis according to the second region data and the preset near-bottom shallow stratum profile data to obtain a target cobalt-rich crust region.

[0034] To achieve the above-mentioned purpose, another aspect of the embodiment of the present application proposes an electronic device, the electronic device comprises:

[0035] At least one processor;

[0036] At least one memory for storing at least one program;

[0037] When the at least one program is executed by the at least one processor, the at least one processor implements the above-mentioned method.

[0038] To achieve the above object, another aspect of the embodiment of the present application provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the above method.

[0039] The embodiment of the present application has at least the following beneficial effects: the present application provides a cobalt-rich crust resource distribution delineation method, system, electronic device and storage medium, which obtains preset geological sedimentary environment data, including oceanic crust age data, sedimentation rate data and preset near-bottom shallow stratigraphic section data. Accordingly, the present application determines first region data according to the preset discriminant model constructed by the coupling effect of the sedimentation rate parameter and the oceanic crust age parameter according to the oceanic crust age data and the sedimentation rate data, and performs topographic constraint correction on the first region data according to the preset topographic constraint parameter to obtain second region data. Finally, the crust region is analyzed according to the second region data and the preset near-bottom shallow stratigraphic section data to obtain the target cobalt-rich crust region, and the cobalt-rich crust resource is delineated. It is easy to understand that the present application first determines the region where the cobalt-rich crust can develop in a large range of horizontal space region according to the oceanic crust age data and the sedimentation rate data through the preset discriminant model, i.e. the first region data, and limits and constrains the region through the preset topographic constraint parameter to improve the accuracy of the cobalt-rich crust resource distribution delineation, obtain the second region data, and then analyze the crust region according to the second region data and the preset near-bottom shallow stratigraphic section data to determine the depth and morphology of the cobalt-rich crust deposit in the vertical space, so as to effectively improve the accuracy of the cobalt-rich crust delineation and improve the efficiency of the cobalt-rich crust resource distribution delineation. BRIEF DESCRIPTION OF DRAWINGS

[0040] Figure 1 is a step flowchart of the cobalt-rich crust resource distribution delineation method provided by the embodiment of the present application;

[0041] Figure 2 is a flowchart of the preset near-bottom shallow stratigraphic section data processing provided by the embodiment of the present application;

[0042] Figure 3 is a step flowchart of the preset discriminant model construction provided by the embodiment of the present application;

[0043] Figure 4 is a step flowchart of determining the first region data according to the oceanic crust age data and the sedimentation rate data through the preset discriminant model provided by the embodiment of the present application;

[0044] Figure 5 is a step flowchart of topographic constraint correction of the first region data according to the preset topographic constraint parameter to obtain the second region data provided by the embodiment of the present application;

[0045] Figure 6is a regional terrain data schematic diagram provided by an embodiment of the present application;

[0046] Figure 7 is a second regional data schematic diagram after removing flat data provided by an embodiment of the present application;

[0047] Figure 8 is a step flowchart of obtaining a target cobalt-rich crust region by constraining the second regional data according to preset near-bottom shallow stratum profile data provided by an embodiment of the present application;

[0048] Figure 9 is a step flowchart of visualizing the target cobalt-rich crust region provided by an embodiment of the present application;

[0049] Figure 10 is a schematic diagram of output three-dimensional ore body provided by an embodiment of the present application;

[0050] Figure 11 is a step flowchart of adjusting parameters of a preset discriminant model provided by an embodiment of the present application;

[0051] Figure 12 is a whole flowchart architecture schematic diagram of cobalt-rich crust resource distribution zoning provided by an embodiment of the present application;

[0052] Figure 13 is a structure schematic diagram of a cobalt-rich crust resource distribution zoning system provided by an embodiment of the present application;

[0053] Figure 14 is a hardware structure schematic diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application is further described in detail below in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementation described in the following exemplary embodiments does not represent all the implementations consistent with the embodiments of the present application, but is only an example of devices and methods consistent with some aspects of the embodiments of the present application as described in the appended claims.

[0055] It can be understood that the terms "first", "second" and the like used in the present application can be used herein to describe various concepts, but unless specifically stated, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of the embodiments of the present application, the first information can also be referred to as the second information, and similarly, the second information can also be referred to as the first information. Depending on the context, the word "if" as used herein can be interpreted as "when" or "upon determining" or "in response to determining".

[0056] The terms "at least one", "multiple", "each", "any" and the like used in the present application include one, two or more, multiple includes two or more, and each refers to each of the corresponding multiple, and any refers to any one of the multiple.

[0057] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as understood by those skilled in the art to which the present application belongs. The terms used herein are only for the purpose of describing the embodiments of the present application and are not intended to limit the present application.

[0058] Before the embodiments of the present application are described in detail, first, some nouns and terms involved in the embodiments of the present application are described, and the nouns and terms involved in the embodiments of the present application are applicable to the following explanations.

[0059] Cobalt-rich crust: A special substance grown on the surface of seabed rocks or rock debris, also known as cobalt crust, iron-manganese crust, manganese crust, cobalt-rich manganese crust, polymetallic crust, cobalt-rich iron-manganese crust, is a crust-shaped ore deposit rich in manganese, cobalt, platinum, rare earth elements and other metal elements on the seabed.

[0060] Cobalt-rich iron-manganese crust (referred to as crust or cobalt-rich crust) appears on the hard rock basement of the entire ocean basin. They are formed on the seabed of seamounts, ridges and the flanks of plateaus and peaks, and are rich in Co, Ti, Mn, Ni, Pt, Zr, Nb, Te, Bi, Mo, W, Th and rare earth elements. The exploration of cobalt-rich crust requires the use of multiple technical means, such as geology, geophysics, geochemistry, etc., and various technical methods need to be closely combined. In the related art, the delineation of cobalt-rich crust development area is mainly based on the identification of acoustic and optical data, but the corresponding delineation accuracy is low, it is difficult to accurately divide the cobalt-rich crust development area, and the work efficiency is not high. For example, the data obtained by the optical method is surface data, which can only judge the distribution of the surface crust, and cannot judge the thickness. By the acoustic thickness measurement method, due to the problem of limited penetration of high-frequency acoustic, it is still impossible to accurately judge the thickness of the crust, which has multiple solutions. In addition, the station sampling method has a series of problems such as low work efficiency and economic efficiency.

[0061] Therefore, the embodiment of the present application provides a cobalt-rich crust resource distribution delineation method and system, an electronic device and a storage medium. The scheme obtains preset geological sedimentary environment data, including oceanic crust age data, sedimentation rate data and preset near-bottom shallow stratigraphic profile data. Accordingly, the embodiment of the present application determines first region data according to a preset discrimination model constructed by the coupling effect of the sedimentation rate parameter and the oceanic crust age parameter based on the oceanic crust age data and the sedimentation rate data, and performs topographic constraint correction on the first region data according to a preset topographic constraint parameter to obtain second region data. Finally, the crust region is analyzed according to the second region data and the preset near-bottom shallow stratigraphic profile data to obtain a target cobalt-rich crust region, thereby realizing the delineation of the cobalt-rich crust resource and effectively improving the efficiency and accuracy of the cobalt-rich crust delineation.

[0062] The cobalt-rich crust resource distribution delineation method provided by the embodiment of the present application relates to the technical field of resource development. The cobalt-rich crust resource distribution delineation method provided by the embodiment of the present application can be applied to a terminal, a server, or software running in the terminal or the server. In some embodiments, the terminal can be a smart phone, a tablet computer, a notebook computer, a desktop computer, a smart speaker, a smart watch, a vehicle-mounted terminal, or the like, but is not limited thereto. The server end can be configured as a stand-alone physical server, a server cluster composed of multiple physical servers, or a distributed system, and can also be configured as a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDNs, and big data and artificial intelligence platforms. The server can also be a node server in a blockchain network. The software can be an application that implements the cobalt-rich crust resource distribution delineation method, but is not limited to the above forms.

[0063] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld devices or portable devices, tablet devices, multi-processor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in a distributed computing environment in which tasks are performed by remote processing devices connected by a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media, including storage devices.

[0064] Figure 1 is an optional flowchart of the cobalt-rich crust resource distribution circle division method provided by the embodiment of the present application, Figure 1 The method in the embodiment of the present application can include, but is not limited to, steps S110 to S140.

[0065] Step S110: Obtain preset geological sedimentary environment data. The preset geological sedimentary environment data includes oceanic crust age data, sedimentation rate data and preset near-bottom shallow stratigraphic section data.

[0066] Step S120: Determine first region data according to the oceanic crust age data and the sedimentation rate data through a preset discrimination model. The preset discrimination model is constructed by the coupling effect of a sedimentation rate parameter and an oceanic crust age parameter.

[0067] Step S130: Perform topographic constraint correction on the first region data according to a preset topographic constraint parameter to obtain second region data.

[0068] Step S140: Perform crust region analysis according to the second region data and the preset near-bottom shallow stratigraphic section data to obtain a target cobalt-rich crust region.

[0069] In the working process of the embodiment, the preset geological sedimentary environment data is first obtained. Specifically, the preset geological sedimentary environment data in the embodiment includes oceanic crust age data, sedimentation rate data and preset near-bottom shallow stratigraphic section data. Correspondingly, the oceanic crust age data in the embodiment refers to data about the formation time of the marine crust (oceanic crust). In the embodiment, the corresponding oceanic crust age data is obtained through a global oceanic crust age model (such as a global grid data), and the general format is: longitude, latitude, oceanic crust age. In addition, the sedimentation rate data in the embodiment refers to information about the accumulation speed of sediments in the process of cobalt-rich crust formation, that is, the thickness of sediments increased in unit time. Correspondingly, the sedimentation rate data is obtained by integrating deep sea drilling (DSDP / ODP), sediment trap and geochemical inversion data, or indirectly calculated by using satellite inversion surface productivity in the embodiment, and the general format is: longitude, latitude, sedimentation rate. Meanwhile, the preset near-bottom shallow stratigraphic section data in the embodiment refers to near-bottom shallow stratigraphic section data, that is, the structure and structure information of the stratum obtained by continuous walking detection of the shallow stratum of the seabed by using physical means such as sound waves. For example, the preset near-bottom shallow stratigraphic section data is collected by a near-bottom shallow stratigraphic section instrument in the embodiment to obtain stratum thickness related information.

[0070] Then, the embodiment of the present application determines the first area data according to the oceanic crust age data and the deposition amount data through a preset discrimination model. Specifically, the preset discrimination model in the embodiment of the present application is used to screen the oceanic crust area suitable for long-term enrichment of metal oxides and easy to form a crust. Accordingly, the preset discrimination model is constructed by coupling the deposition rate parameter and the oceanic crust age parameter. It is easy to understand that different deposition rates and oceanic crust ages have different effects on the enrichment of metal oxides and the formation of crust. Therefore, the preset discrimination model is constructed by the coupling effect between the deposition rate parameter and the oceanic crust age parameter, and then the corresponding oceanic crust age data and deposition rate data are input into the preset discrimination model for analysis and discrimination to obtain the first area data suitable for the formation of cobalt-rich crust. Further, the embodiment of the present application performs terrain constraint correction on the first area data according to the preset terrain constraint parameter to obtain the second area data. Specifically, the preset terrain constraint parameter in the embodiment of the present application refers to the constraint parameter determined according to the terrain where the crust is deposited. Accordingly, the embodiment of the present application limits the range of the first area data by the preset terrain constraint parameter, that is, terrain constraint correction, so as to obtain the second area data. Finally, the embodiment of the present application analyzes the crust area according to the second area data and the preset near-bottom shallow stratum profile data to obtain the target cobalt-rich crust area. Specifically, the embodiment of the present application limits the range of the preset near-bottom shallow stratum profile data by the determined second area data, so as to determine the target cobalt-rich crust area in combination with the lateral space and the longitudinal space, thereby effectively improving the efficiency and accuracy of the cobalt-rich crust ring division.

[0071] It should be noted that, as shown in Figure 2 During the process of obtaining the preset near-bottom shallow stratum profile data, the embodiment of the present application mainly includes the steps of original SEGY data decoding, data filtering, profile drawing, sea bottom line extraction and layer boundary extraction. According to the frequency domain filtering step of shallow profile data processing, the embodiment of the present application effectively removes the influence of random stratum noise, low frequency and high frequency interference, and generates a gray profile based on the filtered result. Then, the data is extracted for the sea bottom line, mainly including structure enhancement filtering of amplitude image, extraction of amplitude layer boundary after enhancement filtering, calculation of local phase image, enhancement processing of phase image and layer boundary extraction based on local phase image. Finally, due to the characteristics that the phase layer boundary is fine but is greatly interfered by non-stratum echo, and the amplitude layer boundary cannot reflect the fine / weak layer boundary but can reflect the layer boundary trend, the embodiment of the present application realizes the comprehensive acquisition of layer boundary by joint amplitude and phase according to the complementarity of the two, and finally outputs the near-bottom shallow stratum profile image with layer boundary information after processing.

[0072] Referring to Figure 3In some examples of the present application, before determining the first region data according to the oceanic crust age data and the sedimentation rate data by the preset discrimination model, the cobalt-rich crust resource distribution circle delimitation method provided by the embodiments of the present application further includes but is not limited to the following steps:

[0073] Step S210: The sedimentation rate parameter and the oceanic crust age parameter are combined and analyzed to determine the coupling effect.

[0074] Step S220: A model discrimination condition is generated by the coupling effect to construct a preset discrimination model.

[0075] In the specific embodiment, the embodiments of the present application combine and analyze the sedimentation rate parameter and the oceanic crust age parameter to determine the corresponding coupling effect, and then generate a model discrimination condition by the coupling effect to construct a preset discrimination model. Specifically, the embodiments of the present application first divide the oceanic crust age parameter into different age segments according to the oceanic crust age division threshold, and different age segments correspond to different types of oceanic crust. For example, the embodiments of the present application divide the oceanic crust with the oceanic crust age parameter in the age segment less than 10 Ma into young oceanic crust, which is close to the mid-ocean ridge, mainly composed of fresh basalt, with rugged terrain (such as seamounts and fault zones), but with frequent hydrothermal activity, which may inhibit the growth of the crust. In addition, the embodiments of the present application divide the oceanic crust with the oceanic crust age parameter in the age segment from 10 Ma to 80 Ma into middle-aged oceanic crust, at which time the basalt is gradually covered by deep-sea sediments, but the low sedimentation rate area may still expose the hard basement, providing an ideal interface for the growth of the crust. Meanwhile, the embodiments of the present application divide the oceanic crust with the oceanic crust age parameter in the age segment greater than 80 Ma into old oceanic crust, at which time the sediment layer is thick, and if the sedimentation rate is high, the basement is buried, and the crust is difficult to form, but in the low sedimentation rate area (such as the CC area of the South Pacific), the crust may still be preserved. Accordingly, the embodiments of the present application divide the sedimentation rate parameter into different sedimentation segments according to the corresponding sedimentation rate threshold, such as when the sedimentation rate is less than 1 cm / kyr, it is divided into low sedimentation rate, at which time the sediment input is small, the basement is exposed to the seawater-sediment interface for a long time, and the crust can grow continuously (such as the seamounts in the equatorial Pacific). In addition, when the sedimentation rate is greater than 3 cm / kyr, it is divided into high sedimentation rate, at which time the rapid sedimentation buries the basement, hindering the formation of the crust (such as the land margin basin or the high productivity area).

[0076] Accordingly, the embodiment of the present application combines the deposition rate of each stage and the age of the oceanic crust to determine the corresponding coupling effect. For example, when the middle-aged oceanic crust and low deposition rate are combined, the base is exposed for a long time and the crust thickness is large (such as the Mid-Pacific seamount group), which is the best combination for the occurrence of cobalt-rich crust. In addition, when the old oceanic crust and low deposition rate are combined, the ancient crust may be preserved, but the new crust is less due to the deep burial of the base. Similarly, when the young oceanic crust and low deposition rate are combined, the terrain is suitable, but hydrothermal activity or volcanic debris may interfere with the composition of the crust. Then, the embodiment of the present application determines the corresponding parameter screening threshold, i.e. the judgment condition of the cobalt-rich crust (model judgment condition), according to the corresponding coupling effect, such as the age of the oceanic crust being 30-70 Ma and the deposition rate being less than 1 cm / kyr, thereby constructing a preset judgment model. In the embodiment of the present application, the parameter screening threshold needs to be calibrated according to the region, such as being relaxed to less than 2 cm / kyr in the CC region.

[0077] Reference Figure 4 In some embodiments of the present application, the first regional data is determined according to the oceanic crust age data and the deposition rate data through the preset judgment model, including but not limited to the following steps:

[0078] Step S310: The oceanic crust age data is classified through a preset age threshold to obtain oceanic crust classification data.

[0079] Step S320: The deposition rate data is classified through a preset deposition threshold to obtain deposition classification data.

[0080] Step S330: The first regional data is obtained by screening the region according to the oceanic crust classification data and the deposition classification data through the preset judgment model.

[0081] In the specific embodiment, the embodiment of the present application first classifies the oceanic crust age data by a preset age threshold to obtain oceanic crust classification data, and classifies the deposition rate data by a preset deposition threshold to obtain deposition classification data. Specifically, the preset age threshold in the embodiment of the present application corresponds to the division threshold of the oceanic crust age parameter in the preset discriminant model construction process. The embodiment of the present application classifies the oceanic crust region corresponding to each oceanic crust age data by the preset age threshold to determine the oceanic crust type corresponding to each oceanic crust region, that is, to obtain the corresponding oceanic crust classification data. For example, when the oceanic crust age data corresponding to the oceanic crust region is less than 10 Ma, it is determined as young oceanic crust, when the oceanic crust age data corresponding to the oceanic crust region is between 10 Ma and 80 Ma, it is determined as middle-aged oceanic crust, and when the oceanic crust age data corresponding to the oceanic crust region is greater than 80 Ma, it is determined as old oceanic crust. Similarly, the preset deposition threshold in the embodiment of the present application also corresponds to the division threshold of the deposition rate parameter in the preset discriminant model construction process, and each deposition rate data corresponding to the oceanic crust data is divided by the corresponding preset deposition threshold to determine the deposition type corresponding to each oceanic crust region, that is, the deposition classification data. For example, when the deposition rate data corresponding to a certain oceanic crust region is less than 1 cm / kyr, the deposition type corresponding to the oceanic crust region is low deposition rate. When the deposition rate data corresponding to a certain oceanic crust region is greater than 3 cm / kyr, the deposition type corresponding to the oceanic crust region is high deposition rate.

[0082] Further, the embodiment of the present application screens the regions by the preset discriminant model according to the oceanic crust classification data and the deposition classification data to obtain first region data. Specifically, the embodiment of the present application inputs the classification results of the oceanic crust age data and the deposition rate data into the preset discriminant model for analysis and screening to screen the regions that meet the occurrence conditions of the cobalt-rich crust and determine the first region data. Illustratively, the embodiment of the present application superimposes the layers corresponding to the oceanic crust age data and the deposition rate data in GIS, and extracts the oceanic crust regions that meet the best coupling effect, that is, the combination of middle-aged oceanic crust and low deposition rate, according to the oceanic crust classification data and the deposition classification data corresponding to each oceanic crust region, to screen the oceanic crust regions that meet the oceanic crust age: 30-70 Ma and the deposition rate:<1 cm / kyr at the same time, to obtain the first region data.

[0083] Referring to Figure 5 In some embodiments of the present application, the first region data is terrain-constrained corrected according to a preset terrain constraint parameter to obtain second region data, including but not limited to the following steps:

[0084] Step S410: performing buffer zone analysis on the first region data and the regional terrain data to obtain third region data.

[0085] Step S420: performing regional screening on the third regional data by preset terrain constraint parameters to obtain second regional data. The preset terrain constraint parameters include a regional slope threshold.

[0086] In the specific embodiment, the embodiment of the present application first performs buffer analysis on the first regional data and regional terrain data to obtain third regional data, and then performs regional screening on the third regional data by preset terrain constraint parameters to obtain second regional data. Specifically, the regional terrain data in the embodiment of the present application refers to Digital Terrain Model (DTM) data, including accuracy, latitude, depth and other data. Since the crust mainly occurs in seamounts, sea platforms and other terrains (water depth of 2000 to 3000 meters), it is necessary to superimpose regional terrain data to further limit the range. Accordingly, the embodiment of the present application superimposes the first regional data obtained by regional screening on the regional terrain data and performs buffer analysis to obtain data with a water depth within a preset range (such as a range of 2-3 kilometers), i.e., third regional data, as shown in FIG. 4. Figure 6 Figure 6 FIG. 4 is a diagram of the third regional data obtained by performing buffer analysis on the first regional data and regional terrain data. As shown in FIG. 4, the first regional data corresponds to terrain data DTM (the color depth represents the depth of water), and the black frame lines A and B regions are the data range of the first regional data. In addition, the preset terrain constraint parameters in the embodiment of the present application include a regional slope threshold, i.e., a terrain slope threshold. Since the crust is difficult to occur in the deep sea plain terrain, the embodiment of the present application further determines the cobalt-rich crust region by eliminating the plain region in the third regional data. For example, the embodiment of the present application eliminates the region with a slope less than 10 degrees in the third regional data, thereby obtaining a region that meets the crust occurrence terrain condition, i.e., the second regional data, as shown in FIG. 5. Figure 7 Figure 7 The medium-high thickness development area is the colored part within the A and B regions in the figure, i.e., the obtained second regional data, including longitude, latitude, water depth, slope, deposition rate, oceanic crust age and other data.

[0087] Referring to Figure 8 In some embodiments of the present application, the target cobalt-rich crust region is obtained by performing crust region analysis on the second regional data and preset near-bottom shallow stratum profile data, including but not limited to the following steps:

[0088] Step S510: determining the target region longitude and latitude information according to the second regional data.

[0089] Step S520: performing regional position constraint on the preset near-bottom shallow stratum profile data by the target region longitude and latitude information to obtain the target cobalt-rich crust region.

[0090] ​​In the embodiment, the embodiment first determines the target area latitude and longitude information according to the second area data, and then performs regional position constraint on the preset near-bottom shallow stratum profile data through the target area latitude and longitude information to obtain the target cobalt-rich crust area. Specifically, the target area latitude and longitude information in the embodiment refers to the longitude and latitude data of the target cobalt-rich crust area, that is, the longitude and latitude data corresponding to the second area data. Correspondingly, the embodiment extracts the corresponding latitude and longitude data, that is, the target area latitude and longitude information, from the second area data, so as to limit the corresponding longitude data and latitude data to the near-bottom shallow stratum profile image corresponding to the preset near-bottom shallow stratum profile data, that is, to perform regional position constraint, so as to delineate the target cobalt-rich crust area in the near-bottom shallow stratum profile image.

[0091] Referring to Figure 9 In some embodiments of the present application, after performing the regional position constraint on the preset near-bottom shallow stratum profile data through the target area latitude and longitude information to obtain the target cobalt-rich crust area, the cobalt-rich crust resource delineation method provided by the embodiment further includes but is not limited to the following steps:

[0092] Step S610: Extracting preset visualization data from the second area data according to the target cobalt-rich crust area. The preset visualization data includes longitude data, latitude data, depth data and amplitude intensity data.

[0093] Step S620: Performing three-dimensional ore body visualization according to the preset visualization data to obtain a cobalt-rich crust three-dimensional image.

[0094] In the embodiment, the embodiment first extracts preset visualization data from the second area data according to the target cobalt-rich crust area. Specifically, the preset visualization data in the embodiment includes longitude data, latitude data, depth data and amplitude intensity data corresponding to the target cobalt-rich crust area. In the embodiment, the longitude data, latitude data and depth data are used as three-dimensional data for visualization, and the amplitude intensity data is associated with the color depth of the visualization display to perform three-dimensional ore body visualization, that is, three-dimensional visualization of the target cobalt-rich crust area delineated, to generate a cobalt-rich crust three-dimensional image, so as to quickly and accurately identify the high-thickness development area of the cobalt-rich crust, as shown in FIG. 6. Figure 10 The coordinate axes X, Y and Z in the figure correspond to longitude data, latitude data and depth data, respectively.

[0095] Referring to Figure 11 In some embodiments of the present application, after performing the regional position constraint on the preset near-bottom shallow stratum profile data through the target area latitude and longitude information to obtain the target cobalt-rich crust area, the cobalt-rich crust resource delineation method provided by the embodiment further includes but is not limited to the following steps:

[0096] Step 710: Collect preset ground condition data corresponding to the target cobalt-rich crust region. The preset ground condition data includes drilling data and camera data.

[0097] Step 720: Verify the target cobalt-rich crust region based on drilling data and camera data, and adjust the parameters of the preset discrimination model based on the corresponding verification results.

[0098] In this specific embodiment, the present invention first collects preset ground condition data corresponding to the target cobalt-rich crust region to verify the target cobalt-rich crust region, and then adjusts the parameters of the preset discrimination model based on the verification results. Specifically, the preset ground condition data in this embodiment refers to data obtained from near-bottom observation or sampling, such as drilling data and camera data. Accordingly, after determining the target cobalt-rich crust region, the present invention performs drilling and image acquisition on the region to analyze the collected drilling data and camera data, determine whether the delineated target cobalt-rich crust region is accurate, and obtain the corresponding verification results. Accordingly, when the delineation error of the target cobalt-rich crust region is determined to be less than the preset error threshold, the verification result is passed. Conversely, when the delineation error of the target cobalt-rich crust region is determined to be greater than the preset error threshold, it indicates that the verification fails, that is, the current delineation error of the cobalt-rich crust resource distribution is too large. At this time, the present invention adjusts the corresponding discrimination conditions and parameters in the preset discrimination model according to the verification results, thereby effectively improving the accuracy of the delineation of the cobalt-rich crust resource distribution.

[0099] It should be noted that, in some embodiments of the present invention, the accuracy of the model can be verified by predicting the distribution of cobalt-rich crust resources in known mining areas and comparing the predicted results with known mining area data. For example, the oceanic crust in the central Pacific seamount area is about 50–60 Ma old, with a deposition rate of <0.5 cm / kyr, a crust coverage of >70%, and an average thickness of 5–8 cm. In contrast, the oceanic crust in the central Indian Ocean basin is >80 Ma old, but has a low deposition rate (<1 cm / kyr), yet thin crust layers (2–3 cm) are still found, indicating that ancient oceanic crust can locally mineralize under low depositional conditions. Furthermore, embodiments of the present invention adjust the model parameters based on the verification results to calibrate and optimize the model.

[0100] The following section provides a detailed introduction and explanation of the embodiments of the present invention, using a specific scenario of delineating the distribution of cobalt-rich crust resources as an example:

[0101] For example, such as Figure 12 As shown, Figure 12An overall flow architecture diagram of the cobalt-rich crust resource distribution delineation provided by the embodiment of the present application is provided. Specifically, the embodiment of the present application first acquires oceanic crust age data, sedimentation rate data and near-bottom shallow stratigraphic profile data, and performs data discrimination through a preset discrimination model. In the embodiment of the present application, the oceanic crust age data is first determined whether it is within a threshold value to filter out the oceanic crust region suitable for high-thickness crust hosting. For example, in the embodiment of the present application, the middle-aged oceanic crust (30Ma-70Ma) region is filtered out, in which the basement rock is stable and the degree of differentiation is moderate, suitable for long-term enrichment of metal oxides and easy to form cobalt-rich crust. Then, the embodiment of the present application further filters out the oceanic crust region within the sedimentation rate threshold value on the basis of the filtered oceanic crust region. Accordingly, the preset discrimination model in the embodiment of the present application is constructed according to the coupling effect between the sedimentation rate and the oceanic crust age, and the oceanic crust region meeting the corresponding parameter threshold value can be filtered out through the preset discrimination model, such as the combination of middle-aged oceanic crust and low sedimentation rate (30-70Ma, sedimentation rate:<1cm / kyr), to obtain first region data including longitude, latitude, sedimentation rate and oceanic crust age data. Then, the embodiment of the present application performs spatial superposition of digital terrain model (DTM) data and the filtered first region data to obtain a region with water depth within the range of 2-3 kilometers, and removes the region with a slope less than 10 degrees, to obtain second region data to filter out the topographic region easy to crust hosting. Further, the embodiment of the present application performs layer boundary processing on the near-bottom shallow stratigraphic profile data through the second region data. In the embodiment of the present application, the near-bottom shallow stratigraphic profile data is constrained according to the longitude and latitude data of the second region data, so as to determine the target cobalt-rich crust region. Meanwhile, the embodiment of the present application extracts the corresponding position data in the near-bottom shallow stratigraphic profile data according to the longitude and latitude data of the second region data, including depth, amplitude intensity, longitude and latitude data, for three-dimensional visualization. Accordingly, the embodiment of the present application performs ground truth test through known mine data, drilling data and camera data to determine whether the current cobalt-rich crust resource distribution delineation is accurate. When the test result error of the ground truth test is less than the corresponding error threshold value, the three-dimensional ore body data obtained by the delineation is output.

[0102] It is easy to understand that the embodiment of the present application first determines the region where the cobalt-rich crust can develop in a large range of horizontal space through the oceanic crust age data and the sedimentation rate data, and then determines the depth and morphology of the cobalt-rich crust deposit in the vertical space to realize the cobalt-rich crust resource distribution delineation. Since the seismic wave belongs to the stress wave category, the near-bottom shallow profile data can finely distinguish the growth boundary of the bedrock and the crust which is difficult to distinguish in traditional data, and significantly improves the scientific accuracy and data accuracy of the cobalt-rich crust development region delineation. The comparison between the present application and other methods is shown in Table 1.

[0103] Table 1

[0104]

[0105] Referring to Figure 13 The embodiment of the present application further provides a cobalt-rich crust resource distribution circle drawing system, which can realize the cobalt-rich crust resource distribution circle drawing method. The system comprises:

[0106] The first module 810 is configured to acquire preset geological sedimentary environment data. The preset geological sedimentary environment data comprises oceanic crust age data, sedimentation rate data and preset near-bottom shallow stratum profile data.

[0107] The second module 820 is configured to determine first region data according to the oceanic crust age data and the sedimentation rate data by using a preset discrimination model. The preset discrimination model is constructed by coupling effects of sedimentation rate parameters and oceanic crust age parameters.

[0108] The third module 830 is configured to perform topographic constraint correction on the first region data according to preset topographic constraint parameters to obtain second region data.

[0109] The fourth module 840 is configured to perform crust region analysis on the second region data and the preset near-bottom shallow stratum profile data to obtain a target cobalt-rich crust region.

[0110] It can be understood that the content in the above method embodiments is applicable to the present system embodiment. The present system embodiment specifically realizes the same functions as the above method embodiments and achieves the same beneficial effects as the above method embodiments.

[0111] The embodiment of the present application further provides an electronic device. The electronic device comprises a memory and a processor. The memory stores a computer program. The processor realizes the cobalt-rich crust resource distribution circle drawing method when executing the computer program. The electronic device can be any intelligent terminal such as a tablet computer or a vehicle-mounted computer.

[0112] It can be understood that the content in the above method embodiments is applicable to the present device embodiment. The present device embodiment specifically realizes the same functions as the above method embodiments and achieves the same beneficial effects as the above method embodiments.

[0113] Referring to Figure 14 , Figure 14 The hardware structure of the electronic device of another embodiment is illustrated. The electronic device comprises:

[0114] The processor 910 can be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits, and is configured to execute related programs to implement the technical solutions provided by the embodiments of the present application.

[0115] The memory 920 can be implemented by a ROM (Read Only Memory), a static storage device, a dynamic storage device, or a RAM (Random Access Memory), and the like. The memory 920 can store an operating system and other application programs. When the technical solutions provided by the embodiments of the present application are implemented by software or firmware, the related program codes are stored in the memory 920 and are called and executed by the processor 910 to implement the resource distribution delineation method of the Co-rich crust provided by the embodiments of the present application.

[0116] The input / output interface 930 is configured to implement information input and output.

[0117] The communication interface 940 is configured to implement the communication interaction between the device and other devices. The communication can be realized by a wired manner (for example, a USB, a network cable, and the like) or a wireless manner (for example, a mobile network, WIFI, Bluetooth, and the like).

[0118] The bus 950 is configured to transmit information between various components (for example, the processor 910, the memory 920, the input / output interface 930, and the communication interface 940) of the device.

[0119] The processor 910, the memory 920, the input / output interface 930, and the communication interface 940 are connected to each other through the bus 950 to realize the communication connection between the device.

[0120] The embodiments of the present application further provide a computer readable storage medium, which stores a computer program. The computer program is executed by a processor to implement the above-mentioned resource distribution delineation method of the Co-rich crust.

[0121] It can be understood that the contents in the above-mentioned method embodiments are all applicable to the present storage medium embodiments. The present storage medium embodiments specifically implement the same functions as the above-mentioned method embodiments, and achieve the same beneficial effects as the above-mentioned method embodiments.

[0122] The memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. In addition, the memory can include a high-speed random access memory, and can also include a non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state memory device. In some embodiments, the memory can optionally include a memory disposed remotely with respect to the processor, and these remote memories can be connected to the processor through a network. Examples of the above network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0123] The embodiments described in the embodiments of the present application are used to more clearly illustrate the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided by the embodiments of the present application. Those skilled in the art can know that, with the evolution of technology and the appearance of new application scenarios, the technical solutions provided by the embodiments of the present application are also applicable to similar technical problems.

[0124] Those skilled in the art can understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and can include more or fewer steps than shown in the figures, or combine certain steps, or different steps.

[0125] The device embodiments described above are only schematic, and the units described as separate components can or can not be physically separate, that is, can be located in one place, or can be distributed on multiple network units. Part or all of the modules can be selected according to actual needs to achieve the purpose of the embodiments of the present application.

[0126] Those skilled in the art can understand that all or some of the steps in the above disclosed method, the functional modules / units in the system and the device can be implemented as software, firmware, hardware and their appropriate combinations.

[0127] The terms "first", "second", "third", "fourth" and the like used in the specification of the present application and the above-described drawings, if any, are used to distinguish similar objects, and do not necessarily have to describe a particular order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0128] It should be understood that, in the application, "at least one" refers to one or more, and "multiple" refers to two or more. "And / or" is used to describe the association relationship of the associated objects, which means that there can be three relationships, for example, "A and / or B" can represent three cases of only A, only B, and A and B existing at the same time, wherein A and B can be singular or plural. The character " / " generally represents an "or" relationship between the associated objects before and after it. "At least one of the following" or similar expressions means any combination of these items, including any combination of single or multiple items. For example, at least one of a, b or c can represent a, b, c, "a and b", "a and c", "b and c", or "a and b and c", wherein a, b, and c can be single or multiple.

[0129] In several embodiments provided in the application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only illustrative, for example, the division of the above units is only a logical function division, and actual implementation can have another division manner, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the displayed or discussed units can be indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0130] The units described above as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, that is, they can be located in one place, or they can be distributed on multiple network units. According to actual needs, some or all of the units can be selected to achieve the purpose of the embodiment scheme.

[0131] In addition, the functional units in each embodiment of the application can be integrated in one processing unit, or each unit can be physically present, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0132] The integrated unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application, essentially or in other words, the part that contributes to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes multiple instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various program storage media.

[0133] The preferred embodiments of the embodiments of the present application are described above with reference to the accompanying drawings, and are not limited to the scope of the embodiments of the present application. Any modifications, equivalent replacements and improvements made by those skilled in the art without departing from the scope and essence of the embodiments of the present application shall be within the scope of the embodiments of the present application.

Claims

1. A method for delineating the resource distribution circle of Co-rich crust, characterized in that, The method comprises the following steps: obtaining preset geological sedimentary environment data; wherein the preset geological sedimentary environment data comprises oceanic crust age data, sedimentation rate data and preset near-bottom shallow stratigraphic profile data; determining first region data according to the oceanic crust age data and the sedimentation rate data through a preset discrimination model; wherein the preset discrimination model is constructed by coupling effects of a sedimentation rate parameter and an oceanic crust age parameter; topographic constraint correction of the first region data according to a preset topographic constraint parameter to obtain second region data; crustal region analysis according to the second region data and the preset near-bottom shallow stratigraphic profile data to obtain a target cobalt-rich crust region; wherein, before the step of determining first region data according to the oceanic crust age data and the sedimentation rate data through a preset discrimination model, the method further comprises: combining analysis of the sedimentation rate parameter and the oceanic crust age parameter to determine the coupling effects; generating model discrimination conditions through the coupling effects to construct the preset discrimination model; wherein, the combining analysis of the sedimentation rate parameter and the oceanic crust age parameter to determine the coupling effects comprises: dividing the oceanic crust age parameter into different age segments according to an oceanic crust age division threshold; wherein different age segments correspond to different oceanic crust segment parameters; dividing the sedimentation rate parameter into different sedimentation segments according to a sedimentation rate threshold; wherein different sedimentation segments correspond to different sedimentation segment parameters; combining the sedimentation segments and the age segments of each stage to determine corresponding combination segment parameters according to the corresponding oceanic crust segment parameters and the sedimentation segment parameters; wherein, generating model discrimination conditions through the coupling effects to construct the preset discrimination model comprises: determining a parameter screening threshold according to the combination segment parameters, and then constructing the preset discrimination model according to the parameter screening threshold; wherein the preset discrimination model matches the age segments and the sedimentation segments through the parameter screening threshold.

2. The method of claim 1, wherein, The step of determining first region data according to the oceanic crust age data and the sedimentation rate data through a preset discrimination model comprises: first classification processing of the oceanic crust age data through a preset age threshold to obtain oceanic crust classification data; second classification processing of the sedimentation rate data through a preset sedimentation threshold to obtain sedimentation classification data; region screening according to the oceanic crust classification data and the sedimentation classification data through the preset discrimination model to obtain the first region data.

3. The method of claim 1, wherein, The step of topographic constraint correction of the first region data according to a preset topographic constraint parameter to obtain second region data comprises: buffer zone analysis of the first region data and regional topographic data to obtain third region data; region screening of the third region data through the preset topographic constraint parameter to obtain the second region data; wherein the preset topographic constraint parameter comprises a regional slope threshold.

4. The method of claim 1, wherein, The step of crustal region analysis according to the second region data and the preset near-bottom shallow stratigraphic profile data to obtain a target cobalt-rich crust region comprises: Determine target area latitude and longitude information according to the second area data; Constrain the preset near-bottom shallow stratum profile data by the target area latitude and longitude information to obtain the target cobalt-rich crust area.

5. The method of claim 4, wherein, After performing the constraining of the preset near-bottom shallow stratum profile data by the target area latitude and longitude information to obtain the target cobalt-rich crust area, the method further comprises: Extract preset visualization data from the second area data according to the target cobalt-rich crust area; wherein the preset visualization data comprises longitude data, latitude data, depth data and amplitude intensity data; Perform three-dimensional ore body visualization according to the preset visualization data to obtain a cobalt-rich crust three-dimensional image.

6. The method of claim 1, wherein, After performing the crust area analysis according to the second area data and the preset near-bottom shallow stratum profile data to obtain the target cobalt-rich crust area, the method further comprises: Collect preset ground live data corresponding to the target cobalt-rich crust area; wherein the preset ground live data comprises drilling data and camera data; Verify the target cobalt-rich crust area according to the drilling data and the camera data to adjust the preset discrimination model according to the corresponding verification result.

7. A system for delineating the distribution of cobalt-rich crust resources, characterized in that, The system comprises: A first module for obtaining preset geological sedimentary environment data; wherein the preset geological sedimentary environment data comprises oceanic crust age data, sedimentation rate data and preset near-bottom shallow stratum profile data; A second module for determining first area data according to the oceanic crust age data and the sedimentation rate data by a preset discrimination model; wherein the preset discrimination model is constructed by coupling effects of a sedimentation rate parameter and an oceanic crust age parameter; A third module for performing topographic constraint correction on the first area data according to preset topographic constraint parameters to obtain second area data; A fourth module for performing crust area analysis according to the second area data and the preset near-bottom shallow stratum profile data to obtain a target cobalt-rich crust area; Before performing the determination of the first area data according to the oceanic crust age data and the sedimentation rate data by the preset discrimination model, the method further comprises: Combining and analyzing the sedimentation rate parameter and the oceanic crust age parameter to determine the coupling effects; Generating model discrimination conditions by the coupling effects to construct the preset discrimination model; The combining and analyzing of the sedimentation rate parameter and the oceanic crust age parameter to determine the coupling effects comprises: Dividing the oceanic crust age parameter into different age segments according to an oceanic crust age division threshold; wherein different age segments correspond to different oceanic crust segment parameters; Dividing the sedimentation rate parameter into different sedimentation segments according to a sedimentation rate threshold; wherein different sedimentation segments correspond to different sedimentation segment parameters; Combining the divided sedimentation segments and age segments of each stage to determine corresponding combined segment parameters according to the corresponding oceanic crust segment parameters and sedimentation segment parameters; Generating model discrimination conditions by the coupling effects to construct the preset discrimination model comprises: The parameter screening threshold is determined according to the combination segmentation parameter, and the preset discrimination model is constructed according to the parameter screening threshold; wherein the preset discrimination model matches the age segmentation and the deposition segmentation through the parameter screening threshold.

8. An electronic device, comprising: Comprise: At least one processor; At least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the method of any one of claims 1-6.

9. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 8. The computer program is executed by the processor to implement the method of any one of claims 1 to 6. The computer program is executed by the processor to implement the method of any one of claims 1 to 6.

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